import d2lzh as d2l
from mxnet import autograd, gluon, nd
from mxnet.gluon import data as gdata, loss as gloss, nn

n_train, n_test, true_w, true_b = 100, 100, [1.2, -3.4, 5.6, ], 5
features = nd.random.normal(shape=(n_train + n_test, 1))
poly_features = nd.concat(features, nd.power(features, 2), nd.power(features, 3))
labels = true_w[0] * poly_features[:, 0] + true_w[1] * poly_features[:, 1] + true_w[2] * poly_features[:, 2] + true_b
labels += nd.random.normal(scale=0.1, shape=labels.shape)
print(features[:2])
print(poly_features[:2])
print(labels[:2])


def semilogy(x_vals, y_vals, x_label, y_label, x2_vals=None, y2_vals=None, legend=None, figsize=(3.5, 2.5)):
    d2l.set_figsize(figsize)
    d2l.plt.xlabel(x_label)
    d2l.plt.ylabel(y_label)
    d2l.plt.semilogy(x_vals, y_vals)
    if x2_vals and y_vals:
        d2l.plt.semilogy(x2_vals, y2_vals, linestyle=':')
        d2l.plt.legend(legend)
    d2l.plt.show()


num_epochs, loss = 100, gloss.L2Loss()


def fit_and_plot(train_features, test_features, train_labels, test_labels):
    net = nn.Sequential()
    net.add(
        nn.Dense(1)
    )
    net.initialize()
    batch_size = min(10, train_labels.shape[0])
    train_iter = gdata.DataLoader(
        gdata.ArrayDataset(train_features, train_labels), batch_size, shuffle=True
    )
    trainer = gluon.Trainer(
        net.collect_params(), 'sgd', {'learning_rate': 0.01}
    )
    train_ls, test_ls = [], []

    for i in range(num_epochs):
        for X, y in train_iter:
            l = 0
            with autograd.record():
                l = loss(net(X), y)
            l.backward()
            trainer.step(batch_size)

        train_ls.append(loss(net(train_features), train_labels).mean().asscalar())
        test_ls.append(loss(net(test_features), test_labels).mean().asscalar())
        print(' epoch: ', i, ' train loss ', train_ls[-1], ', test loss ', test_ls[-1])
    print('final epoch: ', num_epochs, 'train loss ', train_ls[-1], ', test loss ', test_ls[-1])
    semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'loss', range(1, num_epochs + 1), test_ls,
             ['train', 'test'])
    print('weights : ', net[0].weight.data().asnumpy(), '\n b :', net[0].bias.data().asnumpy())


# fit_and_plot(poly_features[:n_train, :], poly_features[n_train:, :],
#              labels[:n_train], labels[n_train:])

#
# fit_and_plot(features[:n_train, :], features[n_train:, :],
#              labels[:n_train], labels[n_train:])


fit_and_plot(poly_features[0:2, :], poly_features[n_train:, :], labels[0:2],
             labels[n_train:])
